A Chaotic Crow Search Algorithm for High-Dimensional Optimization Problems
Abstract
Crow Search Algorithm is an innovative metaheuristic optimization algorithm. In this paper, chaotic mapsare combined into Crow Search Algorithm to increase itsglobal optimization. Ten variant chaotic maps are used and theTent map is found as the best choices for high dimensionalproblems. The novel Chaotic Crow Search Algorithm is reliedon the substitution of a random location of search space andthe awareness parameter of crow with chaotic sequences. Theresults show that the chaotic maps are able to enhance theperformance of the Crow Search Algorithm. Also the novelChaotic Crow Search Algorithm outperforms the conventionalCrow Search Algorithm, the first version of Chaotic Crow SearchThe algorithm, Genetic Algorithm, and Particle SwarmOptimization Algorithm from the point of view of the speedconvergence and the function dimensions
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